Movement Analysis of a Worker in the Forest Cleaning Activities

FORMEC 2019 – Exceeding the Vision: Forest Mechanisation of the Future 6-9 October, 2019 - Sopron, Hungary

MOVEMENT ANALYSIS OF A WORKER IN THE FOREST CLEANING ACTIVITIES

Marin Bačić*, Matija Landekić, Marijan Šušnjar, Mario Šporčić, Zdravko Pandur Faculty of Forestry, University of Zagreb Department of Forest Engineering Svetošimunska cesta 25, 10000 Zagreb, Hrvatska [email protected], [email protected], [email protected], [email protected], [email protected]

Abstract: The main tool used in a forest cleaning is a billhook, a hand tool consisting of a hold and a hooked blade sharpened on both sides. Forest cleaning is physically very tiring and takes place under difficult working conditions (high temperature and humidity, dense vegetation, insects). Moreover, working with billhook is exclusively manual work and its effects on health are mostly unknown. A novelty in Croatia, regarding motor - manual forest cleaning, are battery shears, a more ergonomic and humane approach to forest cleaning. For the purpose of the ergonomic comparison of the two types of forest cleaning methods, a top technology was used – Xsens MVN. Xsens technology consists of sophisticated hardware and software for human movement analysis. It is a full-body human measurement system based on inertial sensors, biomechanical models, and sensor fusion algorithms. The aim of this paper is to present a new and modern method of movement analysis in ongoing research of two mentioned forest cleaning methods, to show challenges of field measurements, and to assume possible application in forestry regarding ergonomic measurements.

Keywords: movement analysis, forest cleaning, billhook, battery shears

1. Introduction

Forest cleaning is a silvicultural procedure within forest tending in which a negative silvicultural selection is made. In traditional and modern forest management it is a necessary step to ensure healthy and high- quality stands. Being a pre harvesting procedure with virtually no direct financial benefits it didn’t get the same “progression treatment” as other harvesting procedures. While the harvesting methods used in thinning, regenerative, and selection felling benefited from technological advancement, methods and tools used in forest cleaning, besides gasoline-powered chainsaws, haven’t changed at all. Tools like billhook, machete, and sickle that were used centuries ago are still in use today. Forest cleaning is carried out with a manual method of work, with the exception of a motor-manual method using a gasoline-powered chainsaw in older stands. This kind of work is mostly carried out in the vegetation period and that means extremely hard working conditions regarding high temperatures and humidity, dense vegetation, insects, etc. The combination of hard-working conditions and sharp manual tools result in a very high risk of physical injuries. Moreover, the facts point to the significant strains on the worker’s body when working with a billhook. The observed movement is very repetitive, and our preliminary studies record over 6000 billhook swings per working day, within 4 hours of effective working time. As an alternative to hand tools in forest cleaning activities, intensive research had been done in Croatian forestry on the application of battery shears. It is assumed that substituting manual method with the motor-manual method can have a positive impact on worker’s health. Nutto et al. (2013) researched the effect of different types of tools, including a hand saw, pruning shears and battery shears, in eucalyptus pruning on the physical workload of the worker and concluded that working with battery shears result with the least physical workload. Bačić et al. (2018) also recorded lower average and maximum heart rates during forest cleaning with battery shears in

65 comparison to manual method with a billhook. Regarding ergonomic studies. aside from measuring physical workload, postural load and recording number and type of injuries, kinetic and motion capture measurements in forestry are not very common. Nowadays, with the help of the new technologies, it is possible to capture, record and analyze every movement. Every movement, especially the repetitive one can have a negative impact on worker’s wellbeing and that is a great “unexplored territory” with a lot of pending questions. The focus of this paper is to present a new method of movement analysis in forest cleaning when using manual (billhook) and motor-manual (battery shears) method. For that purpose, a special suit with integrated IMU sensors was used. This kind of technology was used in sports (Lintmeijer et al., 2018,) and medicine (Held et al., 2018, Longo et al., 2018, Karatsidis et al. 2019). It is assumed that observed values for upper limb angles and kinematics, trunk torsion and body center of mass will significantly differ between two forest cleaning methods.

2. Materials and methods

2.1 Research area

The research was conducted in 10 years old state forest managed by Forest Administration Zagreb, Forest Office Popovača, management unit Popovačke prigorske šume, section 16a (Figure 1). Main tree species were sessile oak (Quercus petraea (Matt.) Liebl.), common hornbeam (Carpinus betulus L.) and common beech (Fagus sylvatica L.). Due to technical limitations, all measurements were conducted on the edge part of the stand, in close vicinity of the road.

Figure 1. Section 16a – measuring site

2.2 Research objects

The worker was a 50 years old male with a body mass of 105 kg and height of 186 cm (Figure 2). At the time of research, the worker had 20 years of working experience in forestry. The worker was dressed in Xsens lycra suit with integrated IMU sensors and standard issued protective clothes.

Figure 2. Worker wearing measuring suit

66 For manual forest cleaning method worker was using standard issued billhook with a mass of 1,5 kg and a length of 1,15 m. For motor-manual method, Stihl ASA 85 battery shears with a mass of 0,98 kg and AP 300 backpack battery with a mass of 1,7 kg was used (Figure 3).

Figure 3. Tools used in forest cleaning

2.3 Research instruments and methods

Kinematic data were recorded using Xsens full-body motion capture suit with 17 IMU sensors (Figure 4). It is a full-body human measurement system based on inertial sensors, biomechanical models and sensor fusion algorithms. Each IMU consists of a 3D accelerometer, a 3D magnetometer, and 3D gyroscope (Xsens Technologies, Enschede, Netherlands).

Figure 4. Positions of IMU sensors on human body

Measured data is to be analyzed in MVN Analyze software capable of real-time 3D animations, graphs and data streaming. Possible 3D data outputs are joint angles, segment kinematics, segment global positions, the body center of mass and sensor data. Since measuring setup required a steady 220 V power supply, a 1 kW generator was used to power laptop and receiver (Figure 5).

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Figure 5. Equipment setup and power source

Before measuring procedure, an obligatory calibration was conducted to “pair” the suit and the worker. The selected worker was instructed to perform forest cleaning using two mentioned methods, around 15 min of each method. Selected trees were marked beforehand. Since the IMU sensors are constantly sending data via the receiver to the laptop, a model of a worker model could be observed in real-time (Figure 6). Because of the dense vegetation which was interfering the communication between the receiver and IMU sensors (worker/suit), the measurement was limited to the edge part of the stand which was closer to receiver and power supply.

Figure 6. Worker and his model

68 It was also observed that the electric power supplied by gasoline generator was not steady enough to properly power laptop and receiver. A 12 V battery with a power inverter would be a better solution.

3. The potential of new technology

Since the data of this research is still in the processing phase, this chapter will be focused on elaborating the potential and validity of the use of the new technology. The essence of the problem is repetitive movement (over 6000 a day) and their effect on the human body. To analyze and valorize a movement of the whole body or a specific body segment, a motion capture technology is needed. In forest cleaning, characteristic movement patterns can be observed. When using a manual method (billhook), a worker is mostly using his upper body. The billhook is an almost exclusively single-handed tool with the exception of two-handed wielding when facing bigger trees. An example of characteristic movements is given on right-handed worker. After approaching the subject tree (or trees), the worker is using his left arm to bend the tree for easier cutting and removal of cut down material. During the movement, his left arm is sometimes fully extended and at an angle of more or less 90° to his trunk (Figure 7).

Figure 7. Position of the left arm (billhook)

His right arm is simultaneously used to swing a billhook toward the base of the tree. During the swing, his right arm is extending until billhook hits the tree (rarely fully extended). The worker is in a slightly bent position (Figure 8). The kinematics of the body segments used in the swinging motion is the core of the problem since that motion is the repetitive one.

Figure 8. Position of the right arm (billhook)

When using battery shears, worker’s left arm is basically doing the same work as with using billhook. The difference is in the right arm where there are no more big swings but only “lock and squeeze” action as with any shears, with exception of these being powered by an electric motor and require minimal force to operate the switch. Since the cutting part of the shears is close to the hand, unlike the billhook blade which is extended with a wooden handle, the worker is in a more bent position (Figure 9).

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Figure 9. Position of both arms (battery shears)

Joint angles (shoulder, elbow, and wrist), upper body angle and right arm kinematics such as velocity and acceleration obtained through motion capture technology would be very beneficial in describing and valorizing the effects of each forest cleaning method on the human body. Since the observed movement is very repetitive and motion capturing logging frequency is high, by using this new technology in real working conditions a great quantity of usable data can be obtained for a very short period of time. Furthermore, previous motion capture technologies were developed for indoor use which is not appropriate for outdoor forestry application, but this technology, with a few limitations like weak reception in dense vegetation, can deliver on that task.

4. Conclusion

Because of its practicality, different data outputs, precision, and the possibility of outdoor use, this technology has a great potential in forestry-related ergonomic measurements. Basically, any type of outdoor forestry work can be recorded, analyzed and improved. Especially the part of forestry works that haven’t changed in terms of tools and methods. As a result, guidelines can be given for new work methods or even tool designs in order to preserve the health and safety of forestry workers. Noticed limitations like weak reception can be avoided if the data is logged directly on-site (memory integrated into the suit itself) and the only real limitation of this technology is the financial cost of the measuring equipment.

5. References

Bačić, M., Šušnjar, M., Pandur, Z., Šporčić, M., Landekić, M. (2018), “Physical workload while working with hedging bill and battery cutter in tending of pedunculate oak,” Proceedings of the International Symposium Ergonomics 2018 – Emphasis on Wellbeing, Zadar, 59-64.

Held, J. P. O., Klaassen, B., Eenhoorn, A., van Beijnum, B. F., Buurke, J. H., Veltink, P. H., Luft, A. R. (2018), “Inertial sensor measurements of upper-limb kinematics in stroke patients in clinic and home environment,” Frontiers in Bioengineering and Biotechnology, 6:27, doi: 10.3389/fbioe.2018.00027. https://bmslab.utwente.nl/knowledgebase/xsens/

Karatsidis, A., Jung, M., Schepers, M., Bellusci, G., de Zee, M., Veltink, P. H., Andersen, M. S. (2019), “Musculoskeletal model-based inverse dynamic analysis under ambulatory conditions using inertial motion capture,” Medical Engineering & Physics, 1-50.

Lintmeijer, L. L., Faber, G. S., Kruk H. R., van Soest, A. J., Hofmijster, M. J. (2018), “An accurate estimation of the horizontal acceleration of a rower’s centre of mass using inertial sensors: a validation,” European Journal of Sport Science, 18:7, 940-946.

70 Longo, A., Meulenbroek, R., Haid, T., Federolf P. (2018), “Postural reconfiguration and cycle-to-cycle variability in patients with work-related musculoskeletal disorders compared to healthy controls and in relation to pain emerging during a repetitive movement task,” Clinical Biomechanics, 54, 103-110.

Nutto, L., Malinovski, R. A., Brunsmeier, M., Schumacher Sant’Anna, F. (2013), “Ergonomic aspects and productivity of different pruning tools for a first pruning lift of Eucalyptus grandis Hill ex Maiden,” Silva Fennica, 47, 1-14.

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